Abstract
The evolution of the Internet and cloud-based technologies have empowered several organizations with the capacity to implement large-scale Internet of Things (IoT)-based ecosystems, such as Industrial IoT (IIoT). The IoT and, by virtue, the IIoT, are vulnerable to new types of threats and intrusions because of the nature of their networks. So it is crucial to develop Intrusion Detection Systems (IDSs) that can provide the security, privacy, and integrity of IIoT networks. In this research, we propose an IDS for IIoT that was implemented using the Genetic Algorithm (GA) for feature selection, and the Random Forest (RF) model was employed in the GA fitness function. The models used for the intrusion detection processes include classifiers such as the RF, Linear Regression (LR), Naïve Bayes (NB), Decision Tree (DT), Extra-Trees (ET), and Extreme Gradient Boosting (XGB). The GA-RF generated 10 feature vectors for the binary classification scheme and 7 feature vectors for the multiclass classification procedure. The UNSW-NB15 is used to assess the effectiveness and the robustness of our proposed approach. The experimental outcomes demonstrated that for the binary modeling process, the GA-RF achieved a test accuracy (TAC) of 87.61% and an Area Under the Curve (AUC) of 0.98, using a feature vector that contained 16 features. These results were superior to existing IDS frameworks.
Highlights
In recent years, the Internet of Things (IoT) paradigm has shown massive adoption by different industries including the medical sector, vehicle manufacturers, home appliances manufacturers, etc
The second feature set, Vm, contained 7 feature vectors that were used for the multiclass modeling process
For the binary classification experiments, the Logistic Regression (LR) algorithm was applied as the baseline model and the following Tree-based models were implemented: Decision Tree (DT), Random Forest (RF), ET, and XGB
Summary
The Internet of Things (IoT) paradigm has shown massive adoption by different industries including the medical sector, vehicle manufacturers, home appliances manufacturers, etc. Kasongo et al.: An advanced Intrusion Detection System for IIoT Based on GA and Tree based Algorithms based Algorithms spoofing attacks, Denial of Service (DoS) attacks, Distributed DoS, Operating System (OS) attacks, jamming attacks, etc To counter these malicious attacks and to guarantee that the active nature of IIoT nodes and the security of IIoT networks are maintained, a lot of organizations are implementing Intrusion Detection Systems (IDSs). In the instance of the filter-based FS method, the selection process relies on the nature of the data and it uses a variety of statistical methods to extract the optimal feature vector. We propose a wrapper-based FS method, based on the Genetic Algorithm (GA) [16] that uses the Random Forest (RF) ML algorithm [17] in its fitness function to generate optimal candidates for feature vectors.
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